All-fabric-based multifunctional textile sensor for detection and discrimination of humidity, temperature, and strain stimuli. Issue 39 (22nd September 2021) - Yang, Shuaitao; Li, Chengwei; Wen, Ningxuan; Xu, Shihong; Huang, Hui; Cong, Tianze; Zhao, Yongpeng; Fan, Zeng; Liu, Kun; Pan, Lujun
Abstract : A simple sewing method was employed to integrate the resistive and capacitive sensors into one device. According to the negative or positive responses of the resistance and capacitance, the applied stimulus can be well-identified. Abstract : Elastic textiles are considered to be ideal materials for constructing wearable devices due to their good air permeability, flexibility, and skin affinity. To date, the construction of textile strain sensors has been extensively researched for human motion monitoring applications. However, the development of multifunctional textile sensors with the ability to distinguish multiple external stimuli is still a challenge. In this work, a strategy is proposed to integrate the resistive and capacitive sensors into one device to broaden the sensing capabilities of textile sensors. Carbon particles are coated on nylon/spandex fabrics as strain sensing layers. A simple sewing method was employed to assemble the conductive fabrics into a pristine intermediary fabric to form a capacitive pressure sensor. The resistance and capacitance of this sandwich structure sensor are both sensitive to humidity, temperature, and strain stimuli. The gauge factors of resistive strain, humidity, and temperature sensors are 62.2, 6.27%/% RH and −5.3%/°C, respectively. According to the negative or positive responses of the resistance and capacitance, the applied stimulus can be well-identified. Both the resistive and capacitive sensors exhibit a stable response in more than 5000 cycles. The resistive and capacitive sensing mechanisms have been analyzed. Because of the wearability, stretchability, and unique multi-sensing capabilities, the sensor can be mounted on the human skin or sewed onto clothes and masks to record a series of physiological signals, such as joint bending, wrist pulse, sweating, and respiration, showing great potential in human–machine interactions, personal health monitoring, and the Internet of things.